Machine vision continues to make rapid advances. Many current machine vision systems utilize a surround view system where image data captured by different image sensors (e.g., different cameras) are combined with each other. To properly couple multiple image sensors, calibration may be performed to establish extrinsic and intrinsic parameters associated with each sensor. Extrinsic parameters include orientations and positions of a sensor relative to the other sensors. Intrinsic parameters include those used in correcting image distortion resulting from the sensor optical path, such as but not limited to, non-linear distortions associated with wide-angle light collection (e.g., commonly known as fisheye distortions). Together, these sensor parameters facilitate mapping an image data frame position to a world position relevant to the platform hosting the machine vision system.
Image sensor calibration is important in the initial installation of a machine vision system into a platform (e.g., during vehicle manufacture). Subsequent calibration (i.e., re-calibration) is also important to maintain machine vision performance throughout the life of the platform. For example, extrinsic sensor parameters are susceptible to drift as a result of platform vibrations, and are also susceptible to large step function changes as a result of platform impacts and/or sensor replacement. Therefore, robust machine vision sensor calibration systems and techniques that do not require extensive manual measurement and/or very controlled calibration environments are advantageous.